If you're a frequent user of popular social media platforms like Twitter, Reddit, or Facebook, you've probably seen memes poking fun at the ways in which AI systems can fail. Computer vision models, designed to interpret visual data, have been known to misidentify cute chihuahuas with big black eyes and button noses as blueberry muffins. Another example involves a fluffy Shiba Inu that was mistaken for a tiger by a computer vision model due to the distinctive stripes on its coat caused by shadows from a nearby gate. While these amusing examples have entertained us, they highlight a more significant problem: computer vision models, no matter how good they are, can fail to work accurately in certain scenarios. Fortunately, there's a solution: computer vision model monitoring.
Misidentifying a Chihuahua for a muffin is unlikely to cause serious harm, but if an autonomous vehicle fails to detect a stop light or a vehicle ahead, it could result in serious injury. This is why computer vision model monitoring is a critical part of the machine learning development pipeline. Monitoring computer vision models involves analyzing their performance after deploying them to production. By doing so, teams can understand the behavior of their models, including their strengths and weaknesses, and focus on refining them to reduce failures.
An important aspect of model monitoring involves understanding the reasons behind model failures. At its core, a computer vision model is a software system, and like any software system, it is prone to occasional failures. Just like machine learning models require monitoring systems to ensure they function reliably once deployed to production, monitoring also plays an important role in traditional software systems. However, machine learning systems, including computer vision models, encounter not only conventional system failures but also those specific to machine learning. Addressing these failures requires observability into the scenarios where the model failed.
Computer vision models are meticulously trained on datasets that strive to mirror the environments and scenarios they will encounter upon deployment in real-world applications. The objective is to enable the model to generalize its learned tasks, such as segmentation or object detection, to previously unseen data. Following initial training, the model is assessed on its ability to generalize when faced with new data not encountered during training.
However, the challenge lies in accurately representing the ever-changing nature of the real world during both training and testing phases of model development. The resources required to capture every conceivable scenario are not only prohibitively expensive, but often impossible to manage. When a model stumbles upon data that diverge significantly from its training data, this is referred to as a data distribution shift. Data points that markedly deviate from the model's distribution are known as outliers, and their presence can cause the model's performance to deteriorate substantially. A vital aspect of model monitoring is detecting these outliers, paving the way for timely and effective remediation.
To provide a clearer illustration of the concept of data distribution shifts, consider the challenges involved in constructing a computer vision system for an autonomous vehicle that can effectively generalize not only across different cities and states but also across various countries and even continents. Such a system must be robust enough to account for differences in road conditions, traffic patterns, signage, and other environmental factors that may vary significantly between regions. As the vehicle’s computer vision model is deployed into production, it is inevitable that the environments and scenarios it encounters will change over time, leading to data distribution shifts. This makes continuous monitoring of the model's performance crucial, particularly in the context of autonomous vehicles, where the consequences of errors can be catastrophic. The potential risks associated with these errors highlight the importance of proactively detecting and addressing any data drifts to maintain optimal performance and safety.
The fundamental reason for monitoring computer vision models lies in the inevitable performance decay they experience over time in production.As the world and the data landscape evolve, the model's effectiveness can be adversely impacted. Regular monitoring allows developers to track performance metrics, identify issues, and make necessary adjustments to the model, ensuring it remains accurate and efficient in its predictions.
Monitoring also plays an important role in improving resource allocation. Neglecting model monitoring in computer vision can prove costly in the long run, as the model's performance wanes, and updating it becomes increasingly difficult and expensive. Monitoring computer vision models helps maintain their performance and optimizes resource allocation, ensuring the models remain relevant and useful.
Moreover, computer vision model monitoring is essential for ensuring not only the reliability and safety of deployed models but also for upholding data ethics. Consistently tracking their performance allows for the prompt identification of anomalies or degradation, enabling quick adjustments and preventing potential hazards. Gaining insight into where models fail is crucial for identifying and addressing biases in computer vision that may have arisen from the training data or the model's architecture. By closely monitoring performance, it becomes possible to create fair and unbiased models, ultimately fostering a more equitable, ethical, and safe application of artificial intelligence in computer vision systems.
Picking the right metrics to focus on will change depending on the task the model is trained to do, and the type of data that you are working with. Here is a list of some of the important metrics to consider when monitoring computer vision models:
Monitoring computer vision models effectively requires an array of tools to measure, track, and analyze the performance of the models in production. These tools usually consist of logs and dashboards to visualize metrics and data, making it easier to understand relationships among numbers and making monitoring accessible to non-engineers. Model monitoring tools also often involve alerting mechanisms to notify people anomalies are detected.
As machine learning models are increasingly being deployed in production environments, the demand for model monitoring and other MLOps tools has grown significantly. Companies such as Arthur, Arize AI and Fiddler have released platforms for model monitoring, observability and explainability.
Manot provides computer vision model monitoring and observability features. Manot persistently monitors the performance of computer vision models during both pre-production and production phases. It is designed to observe and assess models in real-world settings, promptly identifying areas where the model may underperform. Manot collects these outliers, detects biases, and proposes new data samples to enhance the model's training dataset, ultimately improving its performance. By implementing this process during pre-production, teams can deploy their models with increased confidence in their reliability, as they know they have patched a number of the model’s blind spots. Unlike other platforms, Manot is focused and optimized for computer vision model monitoring.
Effective computer vision model monitoring involves adopting best practices that not only help identify issues but also improve overall performance and reliability. Start by establishing clear objectives and expectations for the model, and consistently track key metrics that reflect the model's performance. Regularly monitoring these metrics provides valuable insights into the model's effectiveness and allows developers to proactively address issues and optimize the model.
To detect anomalies and deviations from expected behavior, establish baseline performance levels and set thresholds for key metrics. This approach enables triggering alerts and initiating timely corrective actions when performance falls outside the expected range. Continuously update the model with new data and retrain it to ensure it remains relevant and accurate as the data landscape evolves. Regularly evaluate the model against new data and make necessary adjustments to maintain optimal performance.
Monitor data drift to identify changes in the input data distribution over time and update the model when the real-world environment differs significantly from the training data. Evaluating model latency is also crucial for delivering a responsive and efficient system, particularly in real-world applications where timely decision-making is essential.
Finally, foster collaboration and communication among team members by sharing monitoring insights, discussing potential improvements, and maintaining clear documentation of the monitoring process, including key metrics, insights, and actions taken to address issues. By following these best practices, developers can effectively monitor their computer vision models, leading to more accurate, efficient, and trustworthy AI systems.